{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://blog.csdn.net/qq_35649669/article/details/84990183\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "import  pandas as pd\n",
    "import  os\n",
    "from keras.models import Sequential, load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "dataframe = pd.read_csv('./AirPassengers.csv', usecols=[1], engine='python', skipfooter=3)\n",
    "dataset = dataframe.values\n",
    "# 将整型变为float\n",
    "dataset = dataset.astype('float32')\n",
    "#归一化 在下一步会讲解\n",
    "scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "\n",
    "train_size = int(len(dataset) * 0.90)\n",
    "trainlist = dataset[:train_size]\n",
    "testlist = dataset[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back):\n",
    "#这里的look_back与timestep相同\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back)]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back])\n",
    "    return numpy.array(dataX),numpy.array(dataY)\n",
    "#训练数据太少 look_back并不能过大\n",
    "look_back = 1\n",
    "trainX,trainY  = create_dataset(trainlist,look_back)\n",
    "testX,testY = create_dataset(testlist,look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      " - 4s - loss: 0.0674\n",
      "Epoch 2/50\n",
      " - 0s - loss: 0.0409\n",
      "Epoch 3/50\n",
      " - 0s - loss: 0.0258\n",
      "Epoch 4/50\n",
      " - 0s - loss: 0.0186\n",
      "Epoch 5/50\n",
      " - 0s - loss: 0.0159\n",
      "Epoch 6/50\n",
      " - 0s - loss: 0.0150\n",
      "Epoch 7/50\n",
      " - 0s - loss: 0.0145\n",
      "Epoch 8/50\n",
      " - 0s - loss: 0.0141\n",
      "Epoch 9/50\n",
      " - 0s - loss: 0.0139\n",
      "Epoch 10/50\n",
      " - 0s - loss: 0.0136\n",
      "Epoch 11/50\n",
      " - 0s - loss: 0.0135\n",
      "Epoch 12/50\n",
      " - 0s - loss: 0.0131\n",
      "Epoch 13/50\n",
      " - 0s - loss: 0.0130\n",
      "Epoch 14/50\n",
      " - 0s - loss: 0.0126\n",
      "Epoch 15/50\n",
      " - 0s - loss: 0.0126\n",
      "Epoch 16/50\n",
      " - 0s - loss: 0.0122\n",
      "Epoch 17/50\n",
      " - 0s - loss: 0.0122\n",
      "Epoch 18/50\n",
      " - 0s - loss: 0.0119\n",
      "Epoch 19/50\n",
      " - 0s - loss: 0.0119\n",
      "Epoch 20/50\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 21/50\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 22/50\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 23/50\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 24/50\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 25/50\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 26/50\n",
      " - 0s - loss: 0.0109\n",
      "Epoch 27/50\n",
      " - 0s - loss: 0.0107\n",
      "Epoch 28/50\n",
      " - 0s - loss: 0.0108\n",
      "Epoch 29/50\n",
      " - 0s - loss: 0.0110\n",
      "Epoch 30/50\n",
      " - 0s - loss: 0.0107\n",
      "Epoch 31/50\n",
      " - 0s - loss: 0.0106\n",
      "Epoch 32/50\n",
      " - 0s - loss: 0.0103\n",
      "Epoch 33/50\n",
      " - 0s - loss: 0.0103\n",
      "Epoch 34/50\n",
      " - 0s - loss: 0.0107\n",
      "Epoch 35/50\n",
      " - 0s - loss: 0.0103\n",
      "Epoch 36/50\n",
      " - 0s - loss: 0.0103\n",
      "Epoch 37/50\n",
      " - 0s - loss: 0.0102\n",
      "Epoch 38/50\n",
      " - 0s - loss: 0.0103\n",
      "Epoch 39/50\n",
      " - 0s - loss: 0.0103\n",
      "Epoch 40/50\n",
      " - 0s - loss: 0.0103\n",
      "Epoch 41/50\n",
      " - 0s - loss: 0.0102\n",
      "Epoch 42/50\n",
      " - 0s - loss: 0.0102\n",
      "Epoch 43/50\n",
      " - 0s - loss: 0.0102\n",
      "Epoch 44/50\n",
      " - 0s - loss: 0.0101\n",
      "Epoch 45/50\n",
      " - 0s - loss: 0.0101\n",
      "Epoch 46/50\n",
      " - 0s - loss: 0.0101\n",
      "Epoch 47/50\n",
      " - 0s - loss: 0.0103\n",
      "Epoch 48/50\n",
      " - 0s - loss: 0.0101\n",
      "Epoch 49/50\n",
      " - 0s - loss: 0.0101\n",
      "Epoch 50/50\n",
      " - 0s - loss: 0.0101\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f840ebca750>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\n",
    "testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1] ,1 ))\n",
    "\n",
    "import time\n",
    "# print (time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n",
    "# create and fit the LSTM network\n",
    "model = Sequential()\n",
    "model.add(LSTM(4, input_shape=(None,1)))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(trainX, trainY, epochs=50, batch_size=1, verbose=2)\n",
    "# model.save(os.path.join(\"DATA\",\"Test\" + \".h5\"))\n",
    "# make predictions\n",
    "# print (time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# import time\n",
    "# print (time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n",
    "# model = load_model(os.path.join(\"DATA\",\"Test\" + \".h5\"))\n",
    "# print (time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n",
    "\n",
    "trainPredict = model.predict(trainX)\n",
    "testPredict = model.predict(testX)\n",
    "\n",
    "#反归一化\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY = scaler.inverse_transform(trainY)\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY = scaler.inverse_transform(testY)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[230.]\n",
      " [242.]\n",
      " [209.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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mXsx2Hj3L9BXJLNp0GC+B/lGBjOgSRljNynaHptxIrSrlmTI4ivveX89zn29m6pBo/Sao/kuTexGKT0lj2vI9/JBkNcd4qH0Ij3YKpa5vBbtDU27qprDqPN+jMW8s3cGs1ft4tFOY3SGpEkKTeyEZY1i5+wRTl+1hvTbHUDZ4rJNVYOzNb3bQKqgasSFaYEzpmPsNy84xfLv1KNNW7GHroXTqVC3Po51CGdwmmErl9G+mKl7plzLpM3k1FzOzWTKukxYY8xA65l6ELmfl8MXGVGas2MveE+cJrVGJv93dnH5RAdqxXtmmankfpg6J4a6paxg3dyMfD9MCY55Ok7uTrjTHeH/VPo6mX6JZvaq8e180PSK1OYYqGSLqVeUv/SJ59vPNTPx+J892b2J3SMpGmtzzcXVzjJtC/fnbgBZ01uYYqgQaGBtEfEoa7y5LJirIj24RWmDMU2lyv4ajZy7x3qq9zN1wgAuXs+nWtBajujYgpr42x1Al26t9mrHl0Bmenp/I4rGdCK6uBcY8kSb3q+w9fo4ZK/aycGMqOQb6tKzHSG2OoUqR8j7eTBsSQ6/Jqxj9STyfj2yvBcY8kCZ3h62HzjBteTJLtx7Bx9uLe1sHM7yzNsdQpVNw9YpMHNSKRz+K489fb+Ov/VvYHZIqZh6d3I0xrN93iqnLk1m56zhVypVhZJdwHtHmGMoNdIuozaiu4UxbnkxMfX8GxGiBMU/ikck9J8fwk6M5RoKjOcaz3RtzfzttjqHcyzO3NSLxwGle+mILzepVpWldLTDmKTzqJqas7By+3nyY6cv3svPXswRUq8CILmEM0uYYyo0dP5vBnZNWUbGsN4vGdtQTGDfi8TcxXcrM5rO4g8xwNMdoWKsyEwe1pHfLevhocwyVH2OgFF/2WrNKOd4dEs29M9fx3GebmTZUC4x5ArdO7umXMpm9LoUPVu/nxLkMooKr8UrvZtyqzTFUfjIvwtaF8Mt7cNMoaHmP3REVSusQf8bf0YS/LEni/VX7eKyzFhhzd26Z3E+cy+CD1fv42NEco1PDGozuGkXbMG2OofJxah/EzYKNs+FiGtRsAmXd44qpYR1DrQJj3+6gZVA12oRqgTF3lm9yF5Eg4COgDpADzDTGvCMirwN9HcuOAQ8ZYw479hkPDAOygXHGmO9cFP9vHDx1gfdW7WXeL1ZzjDsi6zCqSwOaB2pzDHUdOTmw5wfrLH339yBe0LQXtH4MQjqW6iGZ3ESEvw9owY4pa3j8kwQWj+tIrSrl7Q5LuUi+E6oiUheoa4xJEJEqQDzQD0g1xqQ7thkHRBhjRopIBDAXaAPUA34AGhljsq/1HoWdUN3161mmLf9fc4y7ogIY0SWccG2Ooa7nwinY+DH8MgtOp0Dl2hDzkPVTtZ7d0bnMjqPp9Ht3Da2CqjF72E3alL0UK9SEqjHmCHDE8fisiCQBAcaY7bk2qwRc+SvRF/jUGJMB7BORPViJfm0hfoc87TtxnglLkvgh6Vcq+HjzYLsQHuuszTFUPg7FWwl96wLIugT1O0C3V6Fpb/B2/ytJmtSpyoR+zXnms0384z+7eOEOLTDmjgo05i4iIUAUsN7xfALwAHAGuNmxWQCwLtduqY5lV7/WcGA4QHBwcAHDtpTxEjYeSOOJWxvyYPsQ/LU5hrqWzEuwbSFseA8OJ4BPJWg1BFoPg9rN7I6u2N0dE0hcShrTVyQTU9+P27TAmNtx+jp3EakMrAAmGGMWXrVuPFDeGPOKiLwLrDXGzHasmwUsNcYsuNZrF2ZY5nJWDmXL6NdKdQ1p+62z9I2z4eIpqNEYWj8KLe+F8p59Q8+lzGwGTP+ZlJMXWDy2I/WrV7I7JFVA1xuWcSoriogPsACYc3Vid/gEuNvxOBUIyrUuEDjsfLgFo4ld/U5OjjUxOmcQvNMK1r5rTYw++DWMWQ83Dff4xA7/KzDmJcKo2QlcyrzmtJgqhfLNjGJdOzgLSDLGTMy1vGGuzfoAOxyPFwH3ikg5EQkFGgIbii5kpa7hwilYMwkmR8GcAXB4I3R+Fp7cAvd8DKGd3ebKl6IS5F+Rt+9pyfYj6bzy1Ta7w1FFyJkx9w7A/cAWEUl0LHsRGCYijbEuhUwBRgIYY7aJyHxgO5AFjLnelTJKFdqhBMcE6efWBGlwe7j1T9CkN5TReZj83NKkNmNuDufdZcnEhPgxKDYo/51UiedRtWWUG8m8BNu+sK5NPxRvTZC2GGSNp9eJtDu6Uic7x/DAB+uJ25/GwtHtaVZP7w0pDa435q7JXZUuaSkQ94F1ffqFk1CjUa4JUk1IhXHiXAa9Jq2mnI8Xix7viG8F978stLTz+MJhqpTLyYHkn6yz9F3fWXeQNulpJfXQLjqOXkRqVC7Hu0OiuGfGOp79bBMz7o/Rch2lmCZ3VXJdOAWJc6zx9LR9UKkWdP4DxDwMvr+7dUIVgZj6/ozv2ZTXF29n5sq9jOgSbndI6gZpclclz+FE6yx9y5UJ0nZwy8vQtI9OkBaDRzqEkJCSxt+/20mroGrcFFbd7pDUDdDkrkqGzEuw/UvrDtJDceBT0RpHb/0o1Glud3QeRUR48+7mJB1J5/G5G1kytiO1qmqBsdJG7wBS9kpLge9fgbcj4IsRcOkM9PgbPJ0Evd/RxG6TKuV9mDY0hnOXsnh87kaysnPsDkkVkJ65q+KXkwN7f4IN78Oub60J0caOCdKwrjpBWkI0rlOFN/pH8tS8Tbz13U7G92xqd0iqADS5q+JzMQ02zrGaYZzaC5VqQqdnIPZh8A20OzqVh7uiAonbn8aMlXuJru9H92Z17A5JOUmTu3K9I5ussfQtn0PWRQhqC11fhIg+UKac3dGpfPypdwRbDp3hD/M30XhsFUJqaIGx0kDH3JVrZGXApnnw/m0wo7NVO73FIBixCoZ9By0GamIvJcqV8ebd+6Lx8hJGzdECY6WFnrmronX6AMR9CAkfwYUTUL0B9HgTWg6GCtXsjk7doCD/ivzfPa14+F+/8Mcvt/LWwJZ2h6TyocldFV5ODuxdBr84JkgBGt0BbR6F0K7gpV8Q3cHNTWox7pYGTPppD7EhftzT+saa7Kjiocld3biLaZD4iXUH6alkqFgDOj5l3UFaTSsLuqMnujVi48HT/PGrbTSr50tkgNbzKam0cJgquCObrTtIN3/mmCC9ybqMMaKvjqN7gJPnMug1eTU+3l58PVYLjNlJC4epwsvKgO1fWVe9pG6AMhWsSdHWj0JdHX/1JNUrl2PKfdHcM2Mtz8zfxMz7Y/Dy0nsTShpN7ur6Th+E+A8h/t/WBKl/OHT/K7QaDBX87I5O2SSmvh8v3dmUP3+9nRkr9zKqqxYYK2k0uavfy8mBfcsdd5B+Yy1r1MNxB+nNOkGqAHiofQhxKWm89d0OWgVVo124FhgrSTS5q/+5eNqaII2bBSf3WBOkHZ607iCtpldGqN8SEf52dwt2HEln7NyNLBnXkdpaYKzE0FMwBUe3wKJxMLEpfDfeGm65ayY8vR26vaKJXV1T5XJlmDY0hvMZWTz+SQKZWmCsxNAzd0+VddmaIP3lfTi4zpogbT7AGnqp18ru6FQp0qh2Fd68uzlPfJrI37/dwUt3RtgdkkKTu+c5k+q4g/TfcP44+IfB7RMgaohOkKob1rdVAHH703hv1T5i6vvRI7Ku3SF5vHyTu4gEAR8BdYAcYKYx5h0ReQvoDVwGkoGHjTGnRSQESAJ2Ol5inTFmpCuCV04yBvYut87Sdy61njfqYd1BGnaLTpCqIvFyr6ZsPnSGZz/bTOM6VQnVAmO2yvcmJhGpC9Q1xiSISBUgHugHBAI/GWOyRORvAMaY5x3JfbExJtLZIPQmJhe5eBo2zbXuID25GypWh+gHrDtI/erbHZ1yQ6lpF+g1eTV1qpbni9EdqFDW2+6Q3Nr1bmLK95TNGHPEGJPgeHwW66w8wBjzH2NMlmOzdVjJXpUER7fC109YE6TfvgDlfeGuGfDUduj2qiZ25TKBflaBsZ2/nuXlL7dSEu6A91QFGnN3nJVHAeuvWvUIMC/X81AR2QikAy8bY1bl8VrDgeEAwcF6NUahZV2GpEXWHaQH10GZ8rkmSKPsjk55kK6NazHuloa88+NuYkP8GNxG/33bwenkLiKVgQXAk8aY9FzLXwKygDmORUeAYGPMSRGJAb4UkWa59wEwxswEZoI1LFO4X8ODnUmF+H9Zd5CePwZ+oXD7X6DVEKjob3d0ykONu7UhCQfSeGXRNpoHaIExOziV3EXEByuxzzHGLMy1/EGgF3CrcXz/MsZkABmOx/Eikgw0AnRQvagYA/tWWGfpO78BkwONukPrxyBcJ0iV/by9hHfujaLXpFWMnB3PkrGd8K2oBcaKkzNXywgwC0gyxkzMtbwH8DzQxRhzIdfymsApY0y2iIQBDYG9RR65J7p0BhLnWle9nNwNFfyh/eMQ+wj4hdgdnVK/4V+pLO8OiWbQjLU8PT+R9x6I1QJjxciZM/cOwP3AFhFJdCx7EZgElAO+t/L/fy957Ay8JiJZQDYw0hhzqqgD9yi/brPO0jfPh8zzEBAD/aZDs7vAR2/3ViVXVLAfL98ZwSuLtjFtRTJjbm5gd0geI9/kboxZDeT153bpNbZfgDWEowrjygTpL7PgwM/WBGnkAGg9DAKi7Y5OKac90K4+cSlp/PM/O4kKqkb7BjXsDskj6B2qJc2ZQ9YEacK/4dyv1nDLba9D1FCdIFWlkojwZv/mJP23wFgn6vjqN05X0+ReEhgD+1Za3Y12LLUmSBveDm0eg/BbdYJUlXqVypVh+tBo+kxZw+OfJDB3eFt8vPW4diVN7na6lA6bPrUmSE/stGq7tBtjTZD6h9odnVJFqkGtKrx5dwvGzd3Im9/s4I+9tMCYK2lyt8Ov262z9E3zrAnSetHQb5pjgrSC3dEp5TJ9WtYjfv8pZq22Coz1bK4FxlxFk3txyboMO762JkhT1oB3OYi82yreFRBjd3RKFZuX7oxgU+oZnvt8M03qVCGsZmW7Q3JLOujlaumHYdkb8H+R8Pkj1h2lt70Gz+yAu6ZpYlcep2wZL6YOicbHWxg1O4ELl7Py30kVmJ65u4IxsH+VdW36jiWOCdLbrDovDbqBl1bKU56tXrUKTBocxQMfbODlL7byz0Etcdwvo4qIJveidCkdNs+zJkiP73BMkI52TJCG2R2dUiVKp4Y1efLWRrz9wy5iQvwYcpNWKy1KmtyLwrEkxx2k8+DyOasKY9+pENlfJ0iVuo6xtzQg4UAaf160neYBvrQIrGZ3SG4j32YdxaFUNuvIzoSkKxOkqx0TpP2t4l2BOo6ulLPSzl+m1+TVACwZ15FqFcvaHFHpUahmHeoq6Udg2V/h7Uj4/GE4cwC6/RmeToK7pmtiV6qA/BwFxo6dvcRT8xLJybH/hNMd6LCMM4yB/auta9OTFoPJhga3Qet3rIlSnSBVqlBaBVXjT70i+ONX25i6fA+P39LQ7pBKPU3u15Nx1nEH6Sw4ngTlq0HbUVbxLp0gVapIDW1rFRib+P0uWgX50bGhFhgrDE3ueTmWZF3xsulTa4K0bivo+y406w9lK9odnVJuSUT4a//mbD+czrhPN7JkXEfq+uoFCTdKx9yvyM6EbV/Av3rB1LaQ8BE06QWP/gjDl1tVGTWxK+VSFcuWYdrQGDIysxkzJ4HLWTl2h1Rq6Zn72aOOHqT/grNHwDcYur0KUfdDJf1aqFRxa1CrMn8b0ILHP9nIX79J4pXezewOqVTyzORujFXf5Zf3rcsZc7Ks0rq93rZK7eoEqVK26tWiHnH70/hwzX5i6vvRq0U9u0MqdTwruWecddxBOguObYfyvnDTSOsO0urhdkenlMrlxZ5N2Zx6muc/30yTOlVpUEsLjBWEZ4y5H9sBS/4A/2wKS54BrzLQZzI8vQO6T9DErlQJVLaMF+8Oiaacjzej58RrgbECct8z9+xM2LnUKguwfxV4l7Xqpbd+DAJjQYsUKVXi1fWtwKR7o7j/g/W8uHALb9/TSguMOcn9kvvZoxD/b4j/0DFBGgS3vmJNkFauaXd0SqkC6tiwBk93a8Q/v99FTIg/97fVAmPOyDe5i0gQ8BFQB8gBZhpj3hGRt4DewGUgGXjYGHPasc94YBiQDYwzxnznmvAdjIGUnx0TpIscE6S3wJ0ToVF3nSBVqpQbc7NVYOz1r7fTIsCXlkHV7A6pxMu3cJiI1AXqGmMSRKQKEA/0AwKBn4wxWSLyNwBjzPMiEgHMBdoA9YAfgEbGmOxrvccNFw7LOJdrgnSbNUHaaqh1B6mOoyvlVk5fuMydk6wCY4vHdsSvkhYYK1ThMGPMEWNMguPxWSAJCDDG/McYc2WGYx1WsgfoC3xqjMkwxuwD9mAl+qL36zZY8jR4eUHvSVbxrh5vaGJXyg1Vq1iWaUOjOX42g6fma4Gx/BToahkRCQGigPVXrXoE+MbxOAA4mGtdqmPZ1a81XETiRCTu+PHjBQnjf4LaWHePjlgFMQ9C2Uo39jpKqVKhRWA1/tQ7guU7jzNl2R67wynRnE7uIlIZWAA8aYxJz7X8JSALmHNlUR67/+5PrDFmpjEm1hgTW7PmDU50iliNMXT2XCmPMeSmYO6KCuDtH3axctcNnhh6AKeSu4j4YCX2OcaYhbmWPwj0AoaY/w3epwJBuXYPBA4XTbhKKU8nIky4K5KGtSrzxKcbOXz6ot0hlUj5JnexLiqdBSQZYybmWt4DeB7oY4y5kGuXRcC9IlJOREKBhsCGog1bKeXJrhQYy8w2jNYCY3ly5sy9A3A/cIuIJDp+egJTgCrA945l0wGMMduA+cB24FtgzPWulFFKqRsRXrMyfx/QgsSDp3ljaZLd4ZQ4+V7nboxZTd7j6Euvs88EYEIh4lJKqXz1bF6XYR1DmbV6H9H1/ejTUguMXeEZtWWUUm7rhTuaEFvfjxcWbGbPsbN2h1NiaHJXSpVqPt5eTLkvmoplvRk5O4HzGVpgDDS5K6XcQB3f8ky6N4q9x88xfuEW8rvz3hNocldKuYX2DWrwzO2NWbTpMB+vS7E7HNtpcldKuY1RXcK5tUktXl+8nY0H0uwOx1aa3JVSbsPLS5g4qBW1q5ZnzJwETp2/bHdIttHkrpRyK74VfZg2JIYT5y7z5LxEsj20wJgmd6WU22ke6MurfZqxctdxJv+02+5wbKHJXSnllga3CaJ/dADv/Lib5TuP2R1OsdPkrpRySyLChH7NaVy7Ck/OS+SQhxUY0+SulHJbFcp6M3VINFmOAmMZWZ5T5kqTu1LKrYXVrMw/BrZg08HTTFjiOQXGNLkrpdxej8i6PNYplI/WpvBV4iG7wykWmtyVUh7huR5NaB3ixwsLtrD7V/cvMKbJXSnlEa4UGKtUrgwjZ8dzzs0LjGlyV0p5jNpVyzN5cBT7TpznhQWb3brAmCZ3pZRHaRdenT90b8zizUf498/77Q7HZTS5K6U8zsjO4XRrWosJS5NIcNMCY5rclVIex8tL+OfAVtTxtQqMnTyXYXdIRU6Tu1LKI10pMHbyvHsWGNPkrpTyWJEBvrzWpxmrdp/gnR922R1Okco3uYtIkIgsE5EkEdkmIk84lg90PM8Rkdhc24eIyEURSXT8THflL6CUUoVxT+sgBsQEMumnPSxzowJjzpy5ZwHPGGOaAm2BMSISAWwF+gMr89gn2RjTyvEzsujCVUqpoiUivN43kiZ1qvDUvERS0y7YHVKRyDe5G2OOGGMSHI/PAklAgDEmyRiz09UBKqWUq1Uo6830oTFku1GBsQKNuYtICBAFrM9n01AR2SgiK0Sk0zVea7iIxIlI3PHjxwsShlJKFbmQGpX4x6CWbE49w+uLt9sdTqE5ndxFpDKwAHjSGJN+nU2PAMHGmCjgaeATEal69UbGmJnGmFhjTGzNmjULGrdSShW57s3qMKJzGLPXHeDLjaW7wJhTyV1EfLAS+xxjzMLrbWuMyTDGnHQ8jgeSgUaFDVQppYrDs90b0ybUn/ELt7CrFBcYc+ZqGQFmAUnGmIlObF9TRLwdj8OAhsDewgaqlFLFoYy3F1MGR5X6AmPOnLl3AO4Hbsl1eWNPEblLRFKBdsASEfnOsX1nYLOIbAI+B0YaY065JHqllHKBWlXLM+W+KFJOXuD5z0tngbEy+W1gjFkNyDVWf5HH9guwhnCUUqrUahtWnWe7N+bNb3YQs8aPRzqG2h1SgegdqkopdQ0jOodxW0Rt3liaRHxK6RqA0OSulFLXICL8Y2BLAvwqMGbORk6UogJjmtyVUuo6fCv4MHVINGkXLvPEpxtLTYExTe5KKZWPZvV8eb1vJGv2nOTt70tHgTFN7kop5YRBrYMYFBvIlGV7+GnHr3aHky9N7kop5aTX+kYSUbcqT83bxMFTJbvAmCZ3pZRyUnkfq8BYjrEKjF3KLLkFxjS5K6VUAQRXr8jEQa3YcugMr5XgAmOa3JVSqoBui6jNyC7hfLL+AAsTUu0OJ0+a3JVS6gb84fZGtA3z58UvtrDj6PUK5dpDk7tSSt2AMt5eTBocRdXyPoyancDZS5l2h/QbmtyVUuoG1apSnin3RXPg1AWeK2EFxjS5K6VUIbQJ9ef5Ho35ZutRZq3eZ3c4/6XJXSmlCumxTmF0b1abN7/ZQdz+klFgTJO7UkoVkojw1sCWBPpVYMwnCRw/a3+BMU3uSilVBKqW92HqkBhOX8hk3NyNZGXn2BqPJnellCoiEfWq8pd+kazde5KJNhcY0+SulFJFaGBsEIPbBDF1eTI/bLevwJgmd6WUKmKv9G5GZEBVnp6fyIGT9hQY0+SulFJFrLyPN9OGxAAw+pN4WwqMaXJXSikXCPKvyNv3tGLroXT+/PW2Yn//fJO7iASJyDIRSRKRbSLyhGP5QMfzHBGJvWqf8SKyR0R2ikh3VwWvlFIl2a1NazO6azhzNxzk8/jiLTDmzJl7FvCMMaYp0BYYIyIRwFagP7Ay98aOdfcCzYAewFQR8S7SqJVSqpR4+rZGtAurzktfbCHpSPEVGMs3uRtjjhhjEhyPzwJJQIAxJskYszOPXfoCnxpjMowx+4A9QJuiDFoppUqLKwXGfCv4MGp2POnFVGCsQGPuIhICRAHrr7NZAHAw1/NUx7KrX2u4iMSJSNzx48cLEoZSSpUqNauU490h0RxMu8hznxVPgTGnk7uIVAYWAE8aY6733ULyWPa738QYM9MYE2uMia1Zs6azYSilVKnUOsSf8Xc04dttR3l/lesLjDmV3EXEByuxzzHGLMxn81QgKNfzQODwjYWnlFLuY1jHUO6IrMOb3+5gwz7XFhhz5moZAWYBScaYiU685iLgXhEpJyKhQENgQ+HCVEqp0k9E+PuAFgT7V+TxTxI4dvaSy97LmTP3DsD9wC0ikuj46Skid4lIKtAOWCIi3wEYY7YB84HtwLfAGGNMyW0RrpRSxahKeR+mDY0m/VImYz9xXYExKQmdQ2JjY01cXJzdYSilVLFZmJDK0/M3MbJLOC/c0eSGXkNE4o0xsXmtK1Oo6JRSSt2Q/tGBbE49Q6BfBZe8viZ3pZSyyat9mrnstbW2jFJKuSFN7kop5YY0uSullBvS5K6UUm5Ik7tSSrkhTe5KKeWGNLkrpZQb0uSulFJuqESUHxCR40BKIV6iBnCiiMIpShpXwWhcBaNxFYw7xlXfGJNnzfQSkdwLS0TirlVfwU4aV8FoXAWjcRWMp8WlwzJKKeWGNLkrpZQbcpfkPtPuAK5B4yoYjatgNK6C8ai43GLMXSml1G+5y5m7UkqpXDS5K6WUGyrRyV1EeojIThHZIyIv5LFeRGSSY/1mEYl2dl8XxzXEEc9mEflZRFrmWrdfRLY4etEWaW9BJ+LqKiJncvXC/ZOz+7o4rmdzxbRVRLJFxN+xzpWf1wcickxEtl5jvV3HV35x2XV85ReXXcdXfnEV+/ElIkEiskxEkkRkm4g8kcc2rj2+jDEl8gfwBpKBMKAssAmIuGqbnsA3gABtgfXO7uviuNoDfo7Hd1yJy/F8P1DDps+rK7D4RvZ1ZVxXbd8b+MnVn5fjtTsD0cDWa6wv9uPLybiK/fhyMq5iP76cicuO4wuoC0Q7HlcBdhV3/irJZ+5tgD3GmL3GmMvAp0Dfq7bpC3xkLOuAaiJS18l9XRaXMeZnY0ya4+k6ILCI3rtQcblo36J+7cHA3CJ67+syxqwETl1nEzuOr3zjsun4cubzuhZbP6+rFMvxZYw5YoxJcDw+CyQBAVdt5tLjqyQn9wDgYK7nqfz+w7nWNs7s68q4chuG9df5CgP8R0TiRWR4EcVUkLjaicgmEflGRK40cCwRn5eIVAR6AAtyLXbV5+UMO46vgiqu48tZxX18Oc2u40tEQoAoYP1Vq1x6fJXkBtmSx7Krr9u81jbO7HujnH5tEbkZ6x9fx1yLOxhjDotILeB7EdnhOPMojrgSsGpRnBORnsCXQEMn93VlXFf0BtYYY3Kfhbnq83KGHceX04r5+HKGHcdXQRT78SUilbH+mDxpjEm/enUeuxTZ8VWSz9xTgaBczwOBw05u48y+rowLEWkBvA/0NcacvLLcGHPY8d9jwBdYX8GKJS5jTLox5pzj8VLAR0RqOLOvK+PK5V6u+srsws/LGXYcX06x4fjKl03HV0EU6/ElIj5YiX2OMWZhHpu49vgq6omEovrB+laxFwjlf5MKza7a5k5+OyGxwdl9XRxXMLAHaH/V8kpAlVyPfwZ6FGNcdfjfjWttgAOOz87Wz8uxnS/WuGml4vi8cr1HCNeeICz248vJuIr9+HIyrmI/vpyJy47jy/F7fwT833W2cenxVWQfrit+sGaTd2HNHL/kWDYSGJnrA3zXsX4LEHu9fYsxrveBNCDR8RPnWB7m+B+1CdhmQ1yPO953E9ZEXPvr7VtccTmePwR8etV+rv685gJHgEyss6VhJeT4yi8uu46v/OKy6/i6blx2HF9YQ2UG2Jzr/1PP4jy+tPyAUkq5oZI85q6UUuoGaXJXSik3pMldKaXckCZ3pZRyQ5rclVLKDWlyV0opN6TJXSml3ND/A+AJSwA5cRIwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trainY)\n",
    "plt.plot(trainPredict[1:])\n",
    "plt.show()\n",
    "plt.plot(testY)\n",
    "\n",
    "print (testY)\n",
    "\n",
    "\n",
    "plt.plot(testPredict[1:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: h5py==2.8.0 in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (2.8.0)\r\n",
      "Requirement already satisfied: six in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from h5py==2.8.0) (1.16.0)\r\n",
      "Requirement already satisfied: numpy>=1.7 in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from h5py==2.8.0) (1.19.2)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install h5py==2.8.0\n",
    "keras 2.2.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}